Neighborhood preserving projections (NPP): a novel linear dimension reduction method

Authors:
Yanwei Pang;Lei Zhang;Zhengkai Liu;Nenghai Yu;Houqiang Li
Affiliations:
Information Processing Center, University of Science and Technology of China, Hefei, China;Microsoft Research Asia, Beijing, China;Information Processing Center, University of Science and Technology of China, Hefei, China;Information Processing Center, University of Science and Technology of China, Hefei, China;Information Processing Center, University of Science and Technology of China, Hefei, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method.